How Big Data is Shaping Adaptive Learning

Educators, administrators and school districts have adopted new digital technologies to improve learning outcomes, especially amid the ongoing COVID-19 pandemic.

Districts and higher education institutions had to quickly pivot to a hybrid teaching model or an entirely virtual learning environment. Companies in the education technology (ed-tech) sector had to step up and deliver new tools to educators and students.

It's expected that the ed-tech industry will experience growth in the coming years, mainly due to the emergence of new technologies and the ever-changing needs of students. One ed-tech trend worth mentioning is big data and how it's shaping adaptive learning.

What Is Adaptive Learning?

Before diving into this big data trend in education, it's critical to understand adaptive learning and how it's traditionally used in the field.

Adaptive learning is an educational technique that provides personalized opportunities to students. Adaptive learning solutions offer efficient, engaging, effective and customized experiences, using a data-driven approach for instruction and remediation.

Students that interact with an adaptive learning solution, such as a computer or web application, find that it adapts to their performance. It provides appropriate content they need at that specific point in time.

Most adaptive learning solutions rely on advanced algorithms, assessments, feedback from students and instructors, and new media types to consistently deliver fresh learning material to the individual student.

Solutions typically fall into three categories: open, closed or hybrid learning systems:

• Open: Users control all configurations, including content decisions and learning objectives.
• Closed: These systems come equipped with off-the-shelf course content where the user has little to no control over what’s included.
• Hybrid: Some configurations come preinstalled, but users have the flexibility to add course content or change things.
  

Open or hybrid adaptive learning systems can be manually adjusted by faculty based on student needs. Over time, adaptive learning systems "learn" from a student's interactions with the program and change the pace and path of learning based on that data.

The Role of Big Data in Adaptive Learning Systems

An adaptive learning solution would be rendered useless without vast amounts of data. These systems can use information regarding student achievements or attention rates to predict future performance or customize learning experiences for students with varying needs.

Big data is a valuable resource for any institution in the education sector. Essentially, institutions can uncover greater insight into student performance by analyzing data amounts that are too large for traditional computing processing methods.

Educators and administrators can review analyzed data to drive decision-making and answer questions they didn't know existed. Several tools can assist institutions in managing this information, such as Hadoop and Spark, two open-source frameworks belonging to the Apache project.

Various publishers and content providers, including McGraw-Hill, Khan Academy, Pearson and Cengage, have created adaptive learning tools that educators can use to their advantage. However, the adoption of these solutions varies depending on several factors. Some individual instructors have adopted them, while others have made institutionwide implementations.

As with many other advanced technologies, adaptive learning solutions tend to improve over time as more data is gathered from student interactions.

Will Big Data Drive Adaptive Learning Solution Adoption?

To answer this question simply, yes — big data analytics will likely drive more widespread adoption of adaptive learning tools.

Adaptive learning brings personalized opportunities directly to a large number of students. Each child receives tailored content that will allow them to go at their own pace.

The education sector is an incredibly data-rich industry, but thus far, institutions have lacked the technology to harness the power of this information. Machine learning (ML), cloud computing and advancements in big data analytics allow schools to capture data, especially regarding adaptive learning, that has yet to be analyzed.

Teachers engaged in adaptive learning techniques and technologies can optimize their students' learning experiences based on their skills and prior knowledge.

Potential Challenges Using Big Data in Adaptive Learning

No technology is perfect — most come with their downsides. Some educators may feel concerned about using big data or adaptive learning solutions effectively and how their role fits into this learning approach.

Algorithms used for big data analytics and within adaptive learning solutions can vary. Providers that fail to outline their products’ details could create gaps in their effectiveness.

Additionally, adaptive learning technologies perform best when students are engaged in entry-level courses regarding specific topics. In other words, an adaptive learning solution may not be effective for every type of class or school subject.

Another potential challenge is that adaptive learning solutions work best in competency-based programs or self-paced learning environments. Regardless of these challenges, it's fascinating to see how educational institutions can leverage big data in the same way major corporations and other industries do daily.

This trend came when educators had to step up to the challengeof teaching students during the pandemic.

More research is needed to address these challenges so publishers, educators and big data professionals can overcome them. A comprehensive review of big data's role in education is still lacking, but it's an area of study worth exploring.

The Future of Big Data in Adaptive Learning

Diversity in the modern classroom makes it critical to meet students where they are and take steps to support and guide them throughout their educational journey.

Adaptive learning solutions can rely on big data and leverage it to improve student learning experiences. The ultimate goal is to cater material to each student so they can perform at their best.